Network Representation Learning

Lab of Media and Network

Department of Computer Science & Technology

Tsinghua University

Non-transitive Hashing with Latent Similarity Components

M Ou, P Cui, F Wang, J Wang, W Zhu. SIGKDD 2015

Motivation

This work embedds entities to a Hamming space based on their semantic similarity.

The semantic similarities between entities
are often non-transitive since they could share different latent
similarity components.
For example, in social networks,
we connect with people for various reasons, such as sharing
common interests, working in the same company, being
alumni and so on. These social connections are
non-transitive if people are connected due to different reasons.

The figure below is another example.

We propose a non-transitive hashing method, namely
Multi-Component Hashing (MuCH), to identify the latent
similarity components to cope with the non-transitive similarity
relationships.
MuCH generates multiple hash tables
with each hash table corresponding to a similarity component,
and preserves the non-transitive similarities in different
hash table respectively.